MovieBase
Tat‐Seng Chua, Sheng Tang, Rémi Trichet, Hung‐Khoon Tan, Yan Song · 2009
The overwhelming amount of multimedia entities shared over the web has given rise to the need for semantic identification and classification of these entities. Numerous research efforts have tackled this problem by developing advanced content analysis techniques as well as leveraging on readily available tags, scripts, and blogs related to these multimedia entities. However, in many cases, especially for event detection and action recognition, the research efforts were hampered by the lack of large scale publicly available benchmarks. To address this problem, this paper presents a large-scale movie corpus named MovieBase that covers full length feature movies as well as huge volume of movie-related video clips downloaded from YouTube. The corpus is designed for research in event detection and action recognition. It offers over 71 hours of videos with a total of 69,129 shots. The corpus has been hand-labeled according to 7 audio and 11 visual concept tags to semantically define 11 event categories under the romantic and violence scenes. The corpus comes with a set of pre-extracted low-level visual, motion, audio as well as high-level features. Related results are furnished as a baseline for the movie event detection task.